code: def VGG16(
      include_top=True,
      weights="imagenet",
      input_tensor=None,
      input_shape=None,
      pooling=None,
      classes=1000,
      classifier_activation="softmax",
  ):
      """Instantiates the VGG16 model.
  
      Reference:
      - [Very Deep Convolutional Networks for Large-Scale Image Recognition](
      https://arxiv.org/abs/1409.1556) (ICLR 2015)
  
      For image classification use cases, see
      [this page for detailed examples](
        https://keras.io/api/applications/#usage-examples-for-image-classification-models).
  
      For transfer learning use cases, make sure to read the
      [guide to transfer learning & fine-tuning](
        https://keras.io/guides/transfer_learning/).
  
      The default input size for this model is 224x224.
  
      Note: each Keras Application expects a specific kind of input preprocessing.
      For VGG16, call `keras.applications.vgg16.preprocess_input` on your
      inputs before passing them to the model.
      `vgg16.preprocess_input` will convert the input images from RGB to BGR,
      then will zero-center each color channel with respect to the ImageNet
      dataset, without scaling.
  
      Args:
          include_top: whether to include the 3 fully-connected
              layers at the top of the network.
          weights: one of `None` (random initialization),
              `"imagenet"` (pre-training on ImageNet),
              or the path to the weights file to be loaded.
          input_tensor: optional Keras tensor
              (i.e. output of `layers.Input()`)
              to use as image input for the model.
          input_shape: optional shape tuple, only to be specified
              if `include_top` is `False` (otherwise the input shape
              has to be `(224, 224, 3)`
              (with `channels_last` data format) or
              `(3, 224, 224)` (with `"channels_first"` data format).
              It should have exactly 3 input channels,
              and width and height should be no smaller than 32.
              E.g. `(200, 200, 3)` would be one valid value.
          pooling: Optional pooling mode for feature extraction
              when `include_top` is `False`.
              - `None` means that the output of the model will be
                  the 4D tensor output of the
                  last convolutional block.
              - `avg` means that global average pooling
                  will be applied to the output of the
                  last convolutional block, and thus
                  the output of the model will be a 2D tensor.
              - `max` means that global max pooling will
                  be applied.
          classes: optional number of classes to classify images
              into, only to be specified if `include_top` is `True`, and
              if no `weights` argument is specified.
          classifier_activation: A `str` or callable. The activation function to
              use on the "top" layer. Ignored unless `include_top=True`. Set
              `classifier_activation=None` to return the logits of the "top"
              layer.  When loading pretrained weights, `classifier_activation`
              can only be `None` or `"softmax"`.
  
      Returns:
          A model instance.
      """
      if not (
          weights in {"imagenet", None} or file_utils.exists(weights)
      ):
          raise ValueError(
              "The `weights` argument should be either "
              "`None` (random initialization), 'imagenet' "
              "(pre-training on ImageNet), "
              "or the path to the weights file to be loaded.  Received: "
              f"weights={weights}"
          )
  
      if weights == "imagenet" and include_top and classes != 1000:
          raise ValueError(
              "If using `weights='imagenet'` with `include_top=True`, "
              "`classes` should be 1000.  "
              f"Received classes={classes}"
          )
  
      # Determine proper input shape
      input_shape = imagenet_utils.obtain_input_shape(
          input_shape,
          default_size=224,
          min_size=32,
          data_format=backend.image_data_format(),
          require_flatten=include_top,
          weights=weights,
      )
  
      if input_tensor is None:
          img_input = layers.Input(shape=input_shape)
      else:
          if not backend.is_keras_tensor(input_tensor):
              img_input = layers.Input(
                  tensor=input_tensor, shape=input_shape
              )
          else:
              img_input = input_tensor
      # Block 1
      x = layers.Conv2D(
          64,
          (3, 3),
          activation="relu",
          padding="same",
          name="block1_conv1",
      )(img_input)
      x = layers.Conv2D(
          64,
          (3, 3),
          activation="relu",
          padding="same",
          name="block1_conv2",
      )(x)
      x = layers.MaxPooling2D(
          (2, 2), strides=(2, 2), name="block1_pool"
      )(x)
  
      # Block 2
      x = layers.Conv2D(
          128,
          (3, 3),
          activation="relu",
          padding="same",
          name="block2_conv1",
      )(x)
      x = layers.Conv2D(
          128,
          (3, 3),
          activation="relu",
          padding="same",
          name="block2_conv2",
      )(x)
      x = layers.MaxPooling2D(
          (2, 2), strides=(2, 2), name="block2_pool"
      )(x)
  
      # Block 3
      x = layers.Conv2D(
          256,
          (3, 3),
          activation="relu",
          padding="same",
          name="block3_conv1",
      )(x)
      x = layers.Conv2D(
          256,
          (3, 3),
          activation="relu",
          padding="same",
          name="block3_conv2",
      )(x)
      x = layers.Conv2D(
          256,
          (3, 3),
          activation="relu",
          padding="same",
          name="block3_conv3",
      )(x)
      x = layers.MaxPooling2D(
          (2, 2), strides=(2, 2), name="block3_pool"
      )(x)
  
      # Block 4
      x = layers.Conv2D(
          512,
          (3, 3),
          activation="relu",
          padding="same",
          name="block4_conv1",
      )(x)
      x = layers.Conv2D(
          512,
          (3, 3),
          activation="relu",
          padding="same",
          name="block4_conv2",
      )(x)
      x = layers.Conv2D(
          512,
          (3, 3),
          activation="relu",
          padding="same",
          name="block4_conv3",
      )(x)
      x = layers.MaxPooling2D(
          (2, 2), strides=(2, 2), name="block4_pool"
      )(x)
  
      # Block 5
      x = layers.Conv2D(
          512,
          (3, 3),
          activation="relu",
          padding="same",
          name="block5_conv1",
      )(x)
      x = layers.Conv2D(
          512,
          (3, 3),
          activation="relu",
          padding="same",
          name="block5_conv2",
      )(x)
      x = layers.Conv2D(
          512,
          (3, 3),
          activation="relu",
          padding="same",
          name="block5_conv3",
      )(x)
      x = layers.MaxPooling2D(
          (2, 2), strides=(2, 2), name="block5_pool"
      )(x)
  
      if include_top:
          # Classification block
          x = layers.Flatten(name="flatten")(x)
          x = layers.Dense(4096, activation="relu", name="fc1")(x)
          x = layers.Dense(4096, activation="relu", name="fc2")(x)
  
          imagenet_utils.validate_activation(
              classifier_activation, weights
          )
          x = layers.Dense(
              classes,
              activation=classifier_activation,
              name="predictions",
          )(x)
      else:
          if pooling == "avg":
              x = layers.GlobalAveragePooling2D()(x)
          elif pooling == "max":
              x = layers.GlobalMaxPooling2D()(x)
  
      # Ensure that the model takes into account
      # any potential predecessors of `input_tensor`.
      if input_tensor is not None:
          inputs = operation_utils.get_source_inputs(input_tensor)
      else:
          inputs = img_input
  
      # Create model.
      model = Functional(inputs, x, name="vgg16")
  
      # Load weights.
      if weights == "imagenet":
          if include_top:
              weights_path = file_utils.get_file(
                  "vgg16_weights_tf_dim_ordering_tf_kernels.h5",
                  WEIGHTS_PATH,
                  cache_subdir="models",
                  file_hash="64373286793e3c8b2b4e3219cbf3544b",
              )
          else:
              weights_path = file_utils.get_file(
                  "vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5",
                  WEIGHTS_PATH_NO_TOP,
                  cache_subdir="models",
                  file_hash="6d6bbae143d832006294945121d1f1fc",
              )
          model.load_weights(weights_path)
      elif weights is not None:
          model.load_weights(weights)
  
      return model
  
file_path: keras/keras/applications/vgg16.py
imports: from keras import backend
  from keras import layers
  from keras.api_export import keras_export
  from keras.applications import imagenet_utils
  from keras.models import Functional
  from keras.ops import operation_utils
  from keras.utils import file_utils
label_desc: Use image node_id 1 for calling a global function with example usage: VGG16(include_top, weights, input_tensor, input_shape, pooling, classes, classifier_activation) and returns: model